An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

Background: In the cloud environment, the satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in a minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumers and service providers. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objectives: The research aims to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, the authors have proposed a new load-balancing algorithm named EBGAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and Ant- Colony algorithm. A fitness value is assigned to all virtual machines based on the availability of resources and load on a virtual machine. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of the amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment. Conclusion: Through experimental results, it can be concluded that the proposed load balancing approach EB-GAP has outperformed other load balancing approaches based on relevant parameters.